PROJECT SUMMARY Access to mental health specialists is difficult for many patients in the U.S., particularly for the poor and those who live in rural communities. Telemental health is one potential solution for this access problem, and there has been rapid growth in the number of live video-based telemental health visits in the U.S. While there are numerous rigorous randomized-control trials comparing telemental health visits to in-person mental health specialist care, there have been little research on how telemental health is being used in real-world settings. The uptake of telemental health has been very uneven geographically, and what explains this variation is also largely unknown. Our mixed-methods proposal centers on filling these gaps in knowledge. Under the first aim, we will conduct a series of descriptive analyses of data on 75 million adults with Medicare or commercial insurance to understand how telemental health is being used in conjunction with in-person care and whether the growth of telemental health has decreased disparities in the use of mental health specialists among disadvantaged populations. Under the second aim, we seek to explain why there is geographic variation in uptake using robust statistical machine learning. Under the third aim, we exploit the variation in uptake to assess whether communities with greater telemental health penetration have experienced improvements in care for patients with mental illness. In the fourth aim we will conduct qualitative interviews with providers to complement the empirical analyses and understand the key contextual factors underlying our findings. Understanding how telemental health is being used, why there is variation in use of telemental health, and whether telemental health use is associated with higher quality care could help drive future telemental health interventions and influence the ongoing policy debate on telemedicine regulations and reimbursement. Together the results of these aims are consistent with the NIMH's expressed interest in innovative delivery models that can improve care for underserved communities.